Overview

Brought to you by YData

Dataset statistics

Number of variables39
Number of observations1092670
Missing cells52040
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory808.4 MiB
Average record size in memory775.8 B

Variable types

Categorical8
Numeric24
Boolean6
Text1

Alerts

gameStatus has constant value "on" Constant
team has constant value "h" Constant
a_centripetal is highly overall correlated with a_tot and 7 other fieldsHigh correlation
a_tot is highly overall correlated with a_centripetal and 9 other fieldsHigh correlation
acceleration is highly overall correlated with a_centripetal and 9 other fieldsHigh correlation
anomaly is highly overall correlated with speed and 1 other fieldsHigh correlation
curvature is highly overall correlated with a_tot and 7 other fieldsHigh correlation
deaccel is highly overall correlated with speedUpHigh correlation
displacement is highly overall correlated with a_centripetal and 10 other fieldsHigh correlation
g_force is highly overall correlated with a_centripetal and 9 other fieldsHigh correlation
g_force_avg is highly overall correlated with a_centripetal and 9 other fieldsHigh correlation
gap is highly overall correlated with displacementHigh correlation
lean is highly overall correlated with a_centripetal and 7 other fieldsHigh correlation
playerId is highly overall correlated with playingPosition and 1 other fieldsHigh correlation
playerShiftNum is highly overall correlated with timestamp and 2 other fieldsHigh correlation
playingPosition is highly overall correlated with playerId and 1 other fieldsHigh correlation
q is highly overall correlated with superframeHigh correlation
radius_curvature is highly overall correlated with a_tot and 7 other fieldsHigh correlation
speed is highly overall correlated with a_centripetal and 10 other fieldsHigh correlation
speedUp is highly overall correlated with deaccelHigh correlation
superframe is highly overall correlated with qHigh correlation
sustained_speed is highly overall correlated with a_centripetal and 10 other fieldsHigh correlation
tagId is highly overall correlated with playerId and 1 other fieldsHigh correlation
timestamp is highly overall correlated with playerShiftNum and 2 other fieldsHigh correlation
toi is highly overall correlated with playerShiftNum and 2 other fieldsHigh correlation
totalDistance is highly overall correlated with playerShiftNum and 2 other fieldsHigh correlation
speedDown_end is highly imbalanced (97.3%) Imbalance
speedUp_start is highly imbalanced (97.6%) Imbalance
gap is highly imbalanced (> 99.9%) Imbalance
skatingEdge is highly imbalanced (58.1%) Imbalance
g_force_peak is highly imbalanced (62.6%) Imbalance
anomaly is highly imbalanced (99.9%) Imbalance
playerShift is highly imbalanced (62.4%) Imbalance
playingPosition has 52010 (4.8%) missing values Missing
q is highly skewed (γ1 = 24.39298355) Skewed
superframe is highly skewed (γ1 = 28.13377783) Skewed
displacement is highly skewed (γ1 = 73.12185658) Skewed
curvature is highly skewed (γ1 = 562.3031545) Skewed
radius_curvature is highly skewed (γ1 = 456.5640366) Skewed
q has 1090659 (99.8%) zeros Zeros
superframe has 1090663 (99.8%) zeros Zeros
playerShiftNum has 303635 (27.8%) zeros Zeros

Reproduction

Analysis started2025-02-18 17:26:08.636323
Analysis finished2025-02-18 17:32:08.261412
Duration5 minutes and 59.63 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

tagId
Categorical

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.4 MiB
01AF
 
52324
3567
 
52314
00EA
 
52243
01C1
 
52189
0169
 
52188
Other values (16)
831412 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4370680
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0012
2nd row0012
3rd row0012
4th row0012
5th row0012

Common Values

ValueCountFrequency (%)
01AF 52324
 
4.8%
3567 52314
 
4.8%
00EA 52243
 
4.8%
01C1 52189
 
4.8%
0169 52188
 
4.8%
14AF 52187
 
4.8%
23E5 52182
 
4.8%
2E53 52170
 
4.8%
01B9 52165
 
4.8%
022F 52165
 
4.8%
Other values (11) 570543
52.2%

Length

2025-02-18T12:32:08.296312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01af 52324
 
4.8%
3567 52314
 
4.8%
00ea 52243
 
4.8%
01c1 52189
 
4.8%
0169 52188
 
4.8%
14af 52187
 
4.8%
23e5 52182
 
4.8%
2e53 52170
 
4.8%
01b9 52165
 
4.8%
022f 52165
 
4.8%
Other values (11) 570543
52.2%

Most occurring characters

ValueCountFrequency (%)
0 829960
19.0%
1 573451
13.1%
3 571099
13.1%
2 518906
11.9%
E 312695
 
7.2%
A 260768
 
6.0%
6 208777
 
4.8%
5 208681
 
4.8%
F 208675
 
4.8%
9 208528
 
4.8%
Other values (5) 469140
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4370680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 829960
19.0%
1 573451
13.1%
3 571099
13.1%
2 518906
11.9%
E 312695
 
7.2%
A 260768
 
6.0%
6 208777
 
4.8%
5 208681
 
4.8%
F 208675
 
4.8%
9 208528
 
4.8%
Other values (5) 469140
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4370680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 829960
19.0%
1 573451
13.1%
3 571099
13.1%
2 518906
11.9%
E 312695
 
7.2%
A 260768
 
6.0%
6 208777
 
4.8%
5 208681
 
4.8%
F 208675
 
4.8%
9 208528
 
4.8%
Other values (5) 469140
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4370680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 829960
19.0%
1 573451
13.1%
3 571099
13.1%
2 518906
11.9%
E 312695
 
7.2%
A 260768
 
6.0%
6 208777
 
4.8%
5 208681
 
4.8%
F 208675
 
4.8%
9 208528
 
4.8%
Other values (5) 469140
10.7%

timestamp
Real number (ℝ)

High correlation 

Distinct52330
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7333201 × 1012
Minimum1.7333175 × 1012
Maximum1.7333228 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:08.344155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.7333175 × 1012
5-th percentile1.7333178 × 1012
Q11.7333188 × 1012
median1.7333201 × 1012
Q31.7333214 × 1012
95-th percentile1.7333225 × 1012
Maximum1.7333228 × 1012
Range5232900
Interquartile range (IQR)2602000

Descriptive statistics

Standard deviation1506262.8
Coefficient of variation (CV)8.6900439 × 10-7
Kurtosis-1.1938434
Mean1.7333201 × 1012
Median Absolute Deviation (MAD)1301000
Skewness0.0059598626
Sum1.8939469 × 1018
Variance2.2688276 × 1012
MonotonicityNot monotonic
2025-02-18T12:32:08.396530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.733320142 × 101221
 
< 0.1%
1.733320983 × 101221
 
< 0.1%
1.733320982 × 101221
 
< 0.1%
1.733320982 × 101221
 
< 0.1%
1.733320982 × 101221
 
< 0.1%
1.733320982 × 101221
 
< 0.1%
1.733320982 × 101221
 
< 0.1%
1.733320982 × 101221
 
< 0.1%
1.733320982 × 101221
 
< 0.1%
1.733320983 × 101221
 
< 0.1%
Other values (52320) 1092460
> 99.9%
ValueCountFrequency (%)
1.733317526 × 10125
 
< 0.1%
1.733317526 × 10128
 
< 0.1%
1.733317526 × 101215
< 0.1%
1.733317526 × 101216
< 0.1%
1.733317526 × 101220
< 0.1%
1.733317526 × 101220
< 0.1%
1.733317526 × 101220
< 0.1%
1.733317526 × 101220
< 0.1%
1.733317526 × 101220
< 0.1%
1.733317526 × 101220
< 0.1%
ValueCountFrequency (%)
1.733322758 × 10123
 
< 0.1%
1.733322758 × 10125
 
< 0.1%
1.733322758 × 10127
 
< 0.1%
1.733322758 × 101210
< 0.1%
1.733322758 × 101217
< 0.1%
1.733322758 × 101218
< 0.1%
1.733322758 × 101219
< 0.1%
1.733322758 × 101219
< 0.1%
1.733322758 × 101219
< 0.1%
1.733322758 × 101219
< 0.1%

x
Real number (ℝ)

Distinct1089802
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.52068558
Minimum-30.499813
Maximum30.458804
Zeros0
Zeros (%)0.0%
Negative574710
Negative (%)52.6%
Memory size106.2 MiB
2025-02-18T12:32:08.523655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-30.499813
5-th percentile-28.015871
Q1-17.992655
median-1.1570832
Q316.256615
95-th percentile26.231971
Maximum30.458804
Range60.958618
Interquartile range (IQR)34.24927

Descriptive statistics

Standard deviation17.731154
Coefficient of variation (CV)-34.053477
Kurtosis-1.183754
Mean-0.52068558
Median Absolute Deviation (MAD)17.028522
Skewness-0.01497511
Sum-568937.51
Variance314.39384
MonotonicityNot monotonic
2025-02-18T12:32:08.570776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-30.41263544 2869
 
0.3%
19.6584748 1
 
< 0.1%
-4.012695962 1
 
< 0.1%
-4.229305745 1
 
< 0.1%
-4.208532796 1
 
< 0.1%
-4.188287271 1
 
< 0.1%
-4.168618793 1
 
< 0.1%
-4.149572644 1
 
< 0.1%
-4.131232755 1
 
< 0.1%
-4.113603221 1
 
< 0.1%
Other values (1089792) 1089792
99.7%
ValueCountFrequency (%)
-30.49981337 1
< 0.1%
-30.4995975 1
< 0.1%
-30.49950601 1
< 0.1%
-30.49867495 1
< 0.1%
-30.49861819 1
< 0.1%
-30.49857766 1
< 0.1%
-30.49842985 1
< 0.1%
-30.49839605 1
< 0.1%
-30.49796418 1
< 0.1%
-30.49784749 1
< 0.1%
ValueCountFrequency (%)
30.45880432 1
< 0.1%
30.45860798 1
< 0.1%
30.45819022 1
< 0.1%
30.45760299 1
< 0.1%
30.45663256 1
< 0.1%
30.45585403 1
< 0.1%
30.45408841 1
< 0.1%
30.45340965 1
< 0.1%
30.45051649 1
< 0.1%
30.45031818 1
< 0.1%

y
Real number (ℝ)

Distinct1090909
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.5178433
Minimum-15.937435
Maximum18.574121
Zeros0
Zeros (%)0.0%
Negative733573
Negative (%)67.1%
Memory size106.2 MiB
2025-02-18T12:32:08.619399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-15.937435
5-th percentile-14.109033
Q1-10.616803
median-3.9682679
Q31.7985229
95-th percentile11.193166
Maximum18.574121
Range34.511556
Interquartile range (IQR)12.415326

Descriptive statistics

Standard deviation7.8419188
Coefficient of variation (CV)-2.2291836
Kurtosis-0.89492229
Mean-3.5178433
Median Absolute Deviation (MAD)6.2101999
Skewness0.36145326
Sum-3843841.9
Variance61.495691
MonotonicityNot monotonic
2025-02-18T12:32:08.667570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.91263544 1757
 
0.2%
-3.832213795 2
 
< 0.1%
0.0390082539 2
 
< 0.1%
-13.26658887 2
 
< 0.1%
-14.32692735 2
 
< 0.1%
-5.463263597 2
 
< 0.1%
-0.4470939209 1
 
< 0.1%
-5.811702582 1
 
< 0.1%
-5.824584027 1
 
< 0.1%
-5.819931687 1
 
< 0.1%
Other values (1090899) 1090899
99.8%
ValueCountFrequency (%)
-15.93743525 1
< 0.1%
-15.93726831 1
< 0.1%
-15.93712893 1
< 0.1%
-15.93664391 1
< 0.1%
-15.9363321 1
< 0.1%
-15.93557926 1
< 0.1%
-15.93504131 1
< 0.1%
-15.93409257 1
< 0.1%
-15.93322899 1
< 0.1%
-15.93220278 1
< 0.1%
ValueCountFrequency (%)
18.57412117 1
< 0.1%
18.54202017 1
< 0.1%
18.50886424 1
< 0.1%
18.47465387 1
< 0.1%
18.43938986 1
< 0.1%
18.40307347 1
< 0.1%
18.36570577 1
< 0.1%
18.3272882 1
< 0.1%
18.28782188 1
< 0.1%
18.24730778 1
< 0.1%

vx
Real number (ℝ)

Distinct1092522
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0024086448
Minimum-7.7707923
Maximum8.1962423
Zeros0
Zeros (%)0.0%
Negative543802
Negative (%)49.8%
Memory size106.2 MiB
2025-02-18T12:32:08.715643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-7.7707923
5-th percentile-1.5023308
Q1-0.087034586
median0.0002931377
Q30.091598633
95-th percentile1.4084802
Maximum8.1962423
Range15.967035
Interquartile range (IQR)0.17863322

Descriptive statistics

Standard deviation0.93828787
Coefficient of variation (CV)-389.55012
Kurtosis10.353097
Mean-0.0024086448
Median Absolute Deviation (MAD)0.089287226
Skewness0.029971802
Sum-2631.8539
Variance0.88038413
MonotonicityNot monotonic
2025-02-18T12:32:08.762685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0021298999 2
 
< 0.1%
-0.0137770961 2
 
< 0.1%
0.0173905564 2
 
< 0.1%
-9.69804 × 10-52
 
< 0.1%
0.0148688575 2
 
< 0.1%
-0.0051715703 2
 
< 0.1%
-0.0021324658 2
 
< 0.1%
-0.003927418 2
 
< 0.1%
-0.1238695187 2
 
< 0.1%
0.0055894811 2
 
< 0.1%
Other values (1092512) 1092650
> 99.9%
ValueCountFrequency (%)
-7.770792256 1
< 0.1%
-7.769239535 1
< 0.1%
-7.766415483 1
< 0.1%
-7.761299816 1
< 0.1%
-7.75459856 1
< 0.1%
-7.747451424 1
< 0.1%
-7.736869859 1
< 0.1%
-7.728275745 1
< 0.1%
-7.710614257 1
< 0.1%
-7.703252811 1
< 0.1%
ValueCountFrequency (%)
8.196242336 1
< 0.1%
8.047609608 1
< 0.1%
7.898353223 1
< 0.1%
7.781380706 1
< 0.1%
7.748277627 1
< 0.1%
7.597179919 1
< 0.1%
7.582533185 1
< 0.1%
7.443849401 1
< 0.1%
7.383119039 1
< 0.1%
7.344369335 1
< 0.1%

vy
Real number (ℝ)

Distinct1092543
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0002255543
Minimum-4.9532433
Maximum4.5678386
Zeros0
Zeros (%)0.0%
Negative547038
Negative (%)50.1%
Memory size106.2 MiB
2025-02-18T12:32:08.812489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4.9532433
5-th percentile-1.1422952
Q1-0.095377996
median-8.248015 × 10-5
Q30.090325995
95-th percentile1.1460379
Maximum4.5678386
Range9.521082
Interquartile range (IQR)0.18570399

Descriptive statistics

Standard deviation0.68307087
Coefficient of variation (CV)-3028.4099
Kurtosis6.7142809
Mean-0.0002255543
Median Absolute Deviation (MAD)0.092844019
Skewness0.062825844
Sum-246.45641
Variance0.46658581
MonotonicityNot monotonic
2025-02-18T12:32:08.859260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0143693432 2
 
< 0.1%
0.076700517 2
 
< 0.1%
0.0104857914 2
 
< 0.1%
-0.0066649719 2
 
< 0.1%
-0.0103128039 2
 
< 0.1%
0.0179808943 2
 
< 0.1%
-0.0292804791 2
 
< 0.1%
0.0984028934 2
 
< 0.1%
-0.2353502597 2
 
< 0.1%
0.0409197556 2
 
< 0.1%
Other values (1092533) 1092650
> 99.9%
ValueCountFrequency (%)
-4.953243331 1
< 0.1%
-4.94755357 1
< 0.1%
-4.935563061 1
< 0.1%
-4.932818648 1
< 0.1%
-4.906341137 1
< 0.1%
-4.898729404 1
< 0.1%
-4.855724901 1
< 0.1%
-4.852694504 1
< 0.1%
-4.793215435 1
< 0.1%
-4.789791001 1
< 0.1%
ValueCountFrequency (%)
4.567838643 1
< 0.1%
4.566195732 1
< 0.1%
4.559416049 1
< 0.1%
4.557057681 1
< 0.1%
4.554186745 1
< 0.1%
4.552652511 1
< 0.1%
4.541058054 1
< 0.1%
4.540753297 1
< 0.1%
4.536608106 1
< 0.1%
4.535621727 1
< 0.1%

q
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13167013
Minimum0
Maximum98
Zeros1090659
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:08.906071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1341944
Coefficient of variation (CV)23.803382
Kurtosis604.00156
Mean0.13167013
Median Absolute Deviation (MAD)0
Skewness24.392984
Sum143872
Variance9.8231746
MonotonicityNot monotonic
2025-02-18T12:32:08.952626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1090659
99.8%
76 106
 
< 0.1%
75 103
 
< 0.1%
79 97
 
< 0.1%
77 96
 
< 0.1%
74 89
 
< 0.1%
78 86
 
< 0.1%
81 86
 
< 0.1%
80 74
 
< 0.1%
73 70
 
< 0.1%
Other values (76) 1204
 
0.1%
ValueCountFrequency (%)
0 1090659
99.8%
6 1
 
< 0.1%
9 1
 
< 0.1%
15 3
 
< 0.1%
16 1
 
< 0.1%
17 2
 
< 0.1%
18 2
 
< 0.1%
19 3
 
< 0.1%
20 4
 
< 0.1%
21 5
 
< 0.1%
ValueCountFrequency (%)
98 2
 
< 0.1%
97 3
 
< 0.1%
96 4
 
< 0.1%
95 7
 
< 0.1%
94 7
 
< 0.1%
93 15
< 0.1%
92 22
< 0.1%
91 23
< 0.1%
90 22
< 0.1%
89 21
< 0.1%

superframe
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct450
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3048176
Minimum0
Maximum1435
Zeros1090663
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:08.997770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1435
Range1435
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32.808116
Coefficient of variation (CV)25.143834
Kurtosis866.67868
Mean1.3048176
Median Absolute Deviation (MAD)0
Skewness28.133778
Sum1425735
Variance1076.3725
MonotonicityNot monotonic
2025-02-18T12:32:09.045114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1090663
99.8%
378 15
 
< 0.1%
303 15
 
< 0.1%
773 14
 
< 0.1%
420 12
 
< 0.1%
651 10
 
< 0.1%
506 10
 
< 0.1%
555 10
 
< 0.1%
950 9
 
< 0.1%
800 9
 
< 0.1%
Other values (440) 1903
 
0.2%
ValueCountFrequency (%)
0 1090663
99.8%
1 5
 
< 0.1%
2 3
 
< 0.1%
38 6
 
< 0.1%
47 4
 
< 0.1%
50 4
 
< 0.1%
55 4
 
< 0.1%
65 5
 
< 0.1%
70 5
 
< 0.1%
78 1
 
< 0.1%
ValueCountFrequency (%)
1435 4
< 0.1%
1428 6
< 0.1%
1393 8
< 0.1%
1391 5
< 0.1%
1383 8
< 0.1%
1374 2
 
< 0.1%
1373 8
< 0.1%
1370 5
< 0.1%
1369 2
 
< 0.1%
1368 7
< 0.1%

speed
Real number (ℝ)

High correlation 

Distinct1092533
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64247345
Minimum2.93626 × 10-5
Maximum9.3483417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:09.093334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.93626 × 10-5
5-th percentile0.010149872
Q10.050335321
median0.1734114
Q30.84623013
95-th percentile2.7385597
Maximum9.3483417
Range9.3483124
Interquartile range (IQR)0.7958948

Descriptive statistics

Standard deviation0.96654192
Coefficient of variation (CV)1.5044076
Kurtosis5.4094445
Mean0.64247345
Median Absolute Deviation (MAD)0.15341052
Skewness2.2023544
Sum702011.47
Variance0.93420328
MonotonicityNot monotonic
2025-02-18T12:32:09.139723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1048214025 2
 
< 0.1%
0.010772415 2
 
< 0.1%
0.0421515427 2
 
< 0.1%
0.0454771134 2
 
< 0.1%
0.0300413012 2
 
< 0.1%
0.0301895943 2
 
< 0.1%
0.0911013551 2
 
< 0.1%
0.007842411 2
 
< 0.1%
0.0105454596 2
 
< 0.1%
0.0134759835 2
 
< 0.1%
Other values (1092523) 1092650
> 99.9%
ValueCountFrequency (%)
2.93626 × 10-51
< 0.1%
3.51253 × 10-51
< 0.1%
3.60098 × 10-51
< 0.1%
5.86068 × 10-51
< 0.1%
6.44784 × 10-51
< 0.1%
6.56055 × 10-51
< 0.1%
7.67525 × 10-51
< 0.1%
7.72989 × 10-51
< 0.1%
8.0837 × 10-51
< 0.1%
9.13078 × 10-51
< 0.1%
ValueCountFrequency (%)
9.34834173 1
< 0.1%
9.15076981 1
< 0.1%
8.952781664 1
< 0.1%
8.754200614 1
< 0.1%
8.554852768 1
< 0.1%
8.353483015 1
< 0.1%
8.201446329 1
< 0.1%
8.151544895 1
< 0.1%
7.980591125 1
< 0.1%
7.948373356 1
< 0.1%

acceleration
Real number (ℝ)

High correlation 

Distinct1092446
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25011944
Minimum4.0492 × 10-6
Maximum3.8675087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:09.186991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.0492 × 10-6
5-th percentile0.0062192205
Q10.029339451
median0.089281192
Q30.30469611
95-th percentile1.0586988
Maximum3.8675087
Range3.8675046
Interquartile range (IQR)0.27535665

Descriptive statistics

Standard deviation0.36845531
Coefficient of variation (CV)1.4731174
Kurtosis7.6341693
Mean0.25011944
Median Absolute Deviation (MAD)0.074995659
Skewness2.4987559
Sum273298.01
Variance0.13575932
MonotonicityNot monotonic
2025-02-18T12:32:09.235359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0315292155 2
 
< 0.1%
0.0749814476 2
 
< 0.1%
0.0065871405 2
 
< 0.1%
0.0101355294 2
 
< 0.1%
0.069999285 2
 
< 0.1%
0.0086492561 2
 
< 0.1%
0.0139160524 2
 
< 0.1%
0.0489016565 2
 
< 0.1%
0.0235718891 2
 
< 0.1%
0.0214766702 2
 
< 0.1%
Other values (1092436) 1092650
> 99.9%
ValueCountFrequency (%)
4.0492 × 10-61
< 0.1%
4.0704 × 10-61
< 0.1%
7.3524 × 10-61
< 0.1%
8.5845 × 10-61
< 0.1%
1.06993 × 10-51
< 0.1%
1.45716 × 10-51
< 0.1%
1.51373 × 10-51
< 0.1%
1.74177 × 10-51
< 0.1%
1.81709 × 10-51
< 0.1%
2.16156 × 10-51
< 0.1%
ValueCountFrequency (%)
3.867508679 1
< 0.1%
3.841212124 1
< 0.1%
3.832498587 1
< 0.1%
3.832046736 1
< 0.1%
3.829227392 1
< 0.1%
3.826394936 1
< 0.1%
3.814890338 1
< 0.1%
3.776467289 1
< 0.1%
3.76427699 1
< 0.1%
3.743261645 1
< 0.1%

ax
Real number (ℝ)

Distinct1092422
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00010546438
Minimum-3.3935699
Maximum3.6006564
Zeros0
Zeros (%)0.0%
Negative544479
Negative (%)49.8%
Memory size106.2 MiB
2025-02-18T12:32:09.283236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.3935699
5-th percentile-0.49272562
Q1-0.046802761
median0.00011769425
Q30.04912283
95-th percentile0.48434606
Maximum3.6006564
Range6.9942263
Interquartile range (IQR)0.095925591

Descriptive statistics

Standard deviation0.3312238
Coefficient of variation (CV)3140.6224
Kurtosis12.052031
Mean0.00010546438
Median Absolute Deviation (MAD)0.047945316
Skewness-0.12078991
Sum115.23777
Variance0.10970921
MonotonicityNot monotonic
2025-02-18T12:32:09.329873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0019371811 2
 
< 0.1%
-0.0563197546 2
 
< 0.1%
0.0181403775 2
 
< 0.1%
0.0318623757 2
 
< 0.1%
0.0014521842 2
 
< 0.1%
-0.0027546614 2
 
< 0.1%
-0.011782618 2
 
< 0.1%
-0.0020024153 2
 
< 0.1%
-0.0036397422 2
 
< 0.1%
-0.0168574084 2
 
< 0.1%
Other values (1092412) 1092650
> 99.9%
ValueCountFrequency (%)
-3.393569918 1
< 0.1%
-3.393512865 1
< 0.1%
-3.385055241 1
< 0.1%
-3.382728266 1
< 0.1%
-3.373878737 1
< 0.1%
-3.367814742 1
< 0.1%
-3.362356971 1
< 0.1%
-3.35942266 1
< 0.1%
-3.341963027 1
< 0.1%
-3.341693945 1
< 0.1%
ValueCountFrequency (%)
3.600656423 1
< 0.1%
3.59502952 1
< 0.1%
3.591146345 1
< 0.1%
3.569571239 1
< 0.1%
3.538511847 1
< 0.1%
3.528487269 1
< 0.1%
3.523791491 1
< 0.1%
3.516997624 1
< 0.1%
3.508039236 1
< 0.1%
3.502625943 1
< 0.1%

ay
Real number (ℝ)

Distinct1092417
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.652319 × 10-6
Minimum-3.3518035
Maximum3.2090252
Zeros0
Zeros (%)0.0%
Negative544218
Negative (%)49.8%
Memory size106.2 MiB
2025-02-18T12:32:09.440358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.3518035
5-th percentile-0.45177551
Q1-0.046220237
median0.00013371135
Q30.048969284
95-th percentile0.44417044
Maximum3.2090252
Range6.5608287
Interquartile range (IQR)0.095189522

Descriptive statistics

Standard deviation0.29767413
Coefficient of variation (CV)180155.37
Kurtosis10.707466
Mean1.652319 × 10-6
Median Absolute Deviation (MAD)0.047590253
Skewness-0.051895003
Sum1.8054394
Variance0.088609889
MonotonicityNot monotonic
2025-02-18T12:32:09.486610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0089849462 2
 
< 0.1%
-0.0004465179 2
 
< 0.1%
-0.0040386138 2
 
< 0.1%
-0.0001356931 2
 
< 0.1%
0.0023512979 2
 
< 0.1%
0.0504163256 2
 
< 0.1%
-0.0001911434 2
 
< 0.1%
0.0006584753 2
 
< 0.1%
0.0067796649 2
 
< 0.1%
0.0008474457 2
 
< 0.1%
Other values (1092407) 1092650
> 99.9%
ValueCountFrequency (%)
-3.351803466 1
< 0.1%
-3.336443213 1
< 0.1%
-3.333107949 1
< 0.1%
-3.322522664 1
< 0.1%
-3.281099936 1
< 0.1%
-3.260312172 1
< 0.1%
-3.224142462 1
< 0.1%
-3.220638349 1
< 0.1%
-3.125633838 1
< 0.1%
-3.09546262 1
< 0.1%
ValueCountFrequency (%)
3.20902521 1
< 0.1%
3.192082652 1
< 0.1%
3.183536191 1
< 0.1%
3.177287624 1
< 0.1%
3.176430038 1
< 0.1%
3.16849372 1
< 0.1%
3.163600438 1
< 0.1%
3.161140119 1
< 0.1%
3.151461273 1
< 0.1%
3.123778086 1
< 0.1%

totalDistance
Real number (ℝ)

High correlation 

Distinct1092650
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1846.3774
Minimum0
Maximum4866.9671
Zeros21
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:09.535375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile129.00744
Q1853.4938
median1762.7178
Q32755.8022
95-th percentile3761.6765
Maximum4866.9671
Range4866.9671
Interquartile range (IQR)1902.3084

Descriptive statistics

Standard deviation1170.1603
Coefficient of variation (CV)0.6337601
Kurtosis-0.92602412
Mean1846.3774
Median Absolute Deviation (MAD)956.76641
Skewness0.2669289
Sum2.0174812 × 109
Variance1369275.2
MonotonicityNot monotonic
2025-02-18T12:32:09.583108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
< 0.1%
3780.953442 1
 
< 0.1%
3780.907301 1
 
< 0.1%
3780.910221 1
 
< 0.1%
3780.912545 1
 
< 0.1%
3780.914438 1
 
< 0.1%
3780.916224 1
 
< 0.1%
3780.918127 1
 
< 0.1%
3780.92035 1
 
< 0.1%
3780.922988 1
 
< 0.1%
Other values (1092640) 1092640
> 99.9%
ValueCountFrequency (%)
0 21
< 0.1%
0.0092248109 1
 
< 0.1%
0.0094556678 1
 
< 0.1%
0.0096531035 1
 
< 0.1%
0.0111993153 1
 
< 0.1%
0.0178414555 1
 
< 0.1%
0.0189231696 1
 
< 0.1%
0.0218876641 1
 
< 0.1%
0.0219182164 1
 
< 0.1%
0.0247367716 1
 
< 0.1%
ValueCountFrequency (%)
4866.967063 1
< 0.1%
4866.954656 1
< 0.1%
4866.942954 1
< 0.1%
4866.931907 1
< 0.1%
4866.921456 1
< 0.1%
4866.911531 1
< 0.1%
4866.90205 1
< 0.1%
4866.892922 1
< 0.1%
4866.884039 1
< 0.1%
4866.875287 1
< 0.1%

displacement
Real number (ℝ)

High correlation  Skewed 

Distinct1091435
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.064553908
Minimum0
Maximum30.10791
Zeros21
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:09.629595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.001006093
Q10.0049985439
median0.017297847
Q30.084607406
95-th percentile0.27385036
Maximum30.10791
Range30.10791
Interquartile range (IQR)0.079608862

Descriptive statistics

Standard deviation0.12695185
Coefficient of variation (CV)1.9666021
Kurtosis13142.966
Mean0.064553908
Median Absolute Deviation (MAD)0.01531747
Skewness73.121857
Sum70536.119
Variance0.016116773
MonotonicityNot monotonic
2025-02-18T12:32:09.675422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
< 0.1%
0.0064503582 3
 
< 0.1%
0.0022367003 3
 
< 0.1%
0.0006612573 3
 
< 0.1%
0.0038797185 3
 
< 0.1%
0.008285735 2
 
< 0.1%
0.0085449761 2
 
< 0.1%
0.0019692846 2
 
< 0.1%
0.0010164988 2
 
< 0.1%
0.0027855585 2
 
< 0.1%
Other values (1091425) 1092627
> 99.9%
ValueCountFrequency (%)
0 21
< 0.1%
4.0053 × 10-61
 
< 0.1%
4.0586 × 10-61
 
< 0.1%
4.0816 × 10-61
 
< 0.1%
4.4572 × 10-61
 
< 0.1%
7.3276 × 10-61
 
< 0.1%
8.0414 × 10-61
 
< 0.1%
8.0731 × 10-61
 
< 0.1%
8.1657 × 10-61
 
< 0.1%
8.3504 × 10-61
 
< 0.1%
ValueCountFrequency (%)
30.10791016 1
< 0.1%
25.64427107 1
< 0.1%
25.08615208 1
< 0.1%
24.80190242 1
< 0.1%
23.87553068 1
< 0.1%
23.852708 1
< 0.1%
21.14646432 1
< 0.1%
20.83386937 1
< 0.1%
20.36402017 1
< 0.1%
19.8614485 1
< 0.1%

playerId
Categorical

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size160.2 MiB
h10
 
52324
h9
 
52314
h35
 
52243
h8
 
52189
h77
 
52188
Other values (16)
831412 

Length

Max length3
Median length3
Mean length2.7631929
Min length2

Characters and Unicode

Total characters3019258
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowh26
2nd rowh26
3rd rowh26
4th rowh26
5th rowh26

Common Values

ValueCountFrequency (%)
h10 52324
 
4.8%
h9 52314
 
4.8%
h35 52243
 
4.8%
h8 52189
 
4.8%
h77 52188
 
4.8%
h91 52187
 
4.8%
h47 52182
 
4.8%
h4 52170
 
4.8%
h15 52165
 
4.8%
h21 52165
 
4.8%
Other values (11) 570543
52.2%

Length

2025-02-18T12:32:09.717070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
h10 52324
 
4.8%
h9 52314
 
4.8%
h35 52243
 
4.8%
h8 52189
 
4.8%
h77 52188
 
4.8%
h91 52187
 
4.8%
h47 52182
 
4.8%
h4 52170
 
4.8%
h15 52165
 
4.8%
h21 52165
 
4.8%
Other values (11) 570543
52.2%

Most occurring characters

ValueCountFrequency (%)
h 1092670
36.2%
1 469031
15.5%
2 260313
 
8.6%
4 208492
 
6.9%
8 208489
 
6.9%
7 156558
 
5.2%
9 156541
 
5.2%
6 156059
 
5.2%
5 154523
 
5.1%
3 104258
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3019258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 1092670
36.2%
1 469031
15.5%
2 260313
 
8.6%
4 208492
 
6.9%
8 208489
 
6.9%
7 156558
 
5.2%
9 156541
 
5.2%
6 156059
 
5.2%
5 154523
 
5.1%
3 104258
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3019258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 1092670
36.2%
1 469031
15.5%
2 260313
 
8.6%
4 208492
 
6.9%
8 208489
 
6.9%
7 156558
 
5.2%
9 156541
 
5.2%
6 156059
 
5.2%
5 154523
 
5.1%
3 104258
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3019258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 1092670
36.2%
1 469031
15.5%
2 260313
 
8.6%
4 208492
 
6.9%
8 208489
 
6.9%
7 156558
 
5.2%
9 156541
 
5.2%
6 156059
 
5.2%
5 154523
 
5.1%
3 104258
 
3.5%

gameStatus
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.4 MiB
on
1092670 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2185340
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowon
2nd rowon
3rd rowon
4th rowon
5th rowon

Common Values

ValueCountFrequency (%)
on 1092670
100.0%

Length

2025-02-18T12:32:09.750194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-18T12:32:09.778615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
on 1092670
100.0%

Most occurring characters

ValueCountFrequency (%)
o 1092670
50.0%
n 1092670
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2185340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1092670
50.0%
n 1092670
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2185340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1092670
50.0%
n 1092670
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2185340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1092670
50.0%
n 1092670
50.0%

skatingAngle
Real number (ℝ)

Distinct1088075
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.064389681
Minimum-180
Maximum180
Zeros4469
Zeros (%)0.4%
Negative554700
Negative (%)50.8%
Memory size106.2 MiB
2025-02-18T12:32:09.812079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-180
5-th percentile-5.9538061
Q1-1.2647381
median-0.024275161
Q31.1700242
95-th percentile5.7053695
Maximum180
Range360
Interquartile range (IQR)2.4347622

Descriptive statistics

Standard deviation7.3620843
Coefficient of variation (CV)-114.3364
Kurtosis181.26924
Mean-0.064389681
Median Absolute Deviation (MAD)1.2172115
Skewness0.17562363
Sum-70356.672
Variance54.200285
MonotonicityNot monotonic
2025-02-18T12:32:09.859630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4469
 
0.4%
180 47
 
< 0.1%
-180 34
 
< 0.1%
1.079558192 2
 
< 0.1%
-0.3288332471 2
 
< 0.1%
-1.973721046 2
 
< 0.1%
-4.772185758 2
 
< 0.1%
-1.153576183 2
 
< 0.1%
0.5046101669 2
 
< 0.1%
-1.401268377 2
 
< 0.1%
Other values (1088065) 1088106
99.6%
ValueCountFrequency (%)
-180 34
< 0.1%
-179.9050879 1
 
< 0.1%
-179.6593106 1
 
< 0.1%
-178.8997313 1
 
< 0.1%
-178.5479301 1
 
< 0.1%
-178.5166683 1
 
< 0.1%
-178.2019817 1
 
< 0.1%
-178.1459803 1
 
< 0.1%
-178.0480504 1
 
< 0.1%
-177.8458531 1
 
< 0.1%
ValueCountFrequency (%)
180 47
< 0.1%
179.9498751 1
 
< 0.1%
179.9393753 1
 
< 0.1%
179.4691667 1
 
< 0.1%
179.3646566 1
 
< 0.1%
179.27514 1
 
< 0.1%
179.1573184 1
 
< 0.1%
178.7928657 1
 
< 0.1%
178.7506964 1
 
< 0.1%
178.5386518 1
 
< 0.1%

speedUp
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size98.9 MiB
False
560899 
True
531771 
ValueCountFrequency (%)
False 560899
51.3%
True 531771
48.7%
2025-02-18T12:32:09.890357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

zone
Text

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size160.0 MiB
2025-02-18T12:32:09.967630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.6554184
Min length1

Characters and Unicode

Total characters2901496
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP33
2nd rowP33
3rd rowP33
4th rowP33
5th rowP33
ValueCountFrequency (%)
0 188257
 
17.2%
n41 80952
 
7.4%
p24 48953
 
4.5%
p21 38195
 
3.5%
p44 37934
 
3.5%
n14 36206
 
3.3%
n51 33458
 
3.1%
p13 28457
 
2.6%
n44 27975
 
2.6%
p35 27851
 
2.5%
Other values (45) 544432
49.8%
2025-02-18T12:32:10.087653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 515267
17.8%
N 482329
16.6%
P 422084
14.5%
1 400941
13.8%
2 364552
12.6%
3 257889
8.9%
5 239468
8.3%
0 188257
 
6.5%
6 27601
 
1.0%
9 3108
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2901496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 515267
17.8%
N 482329
16.6%
P 422084
14.5%
1 400941
13.8%
2 364552
12.6%
3 257889
8.9%
5 239468
8.3%
0 188257
 
6.5%
6 27601
 
1.0%
9 3108
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2901496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 515267
17.8%
N 482329
16.6%
P 422084
14.5%
1 400941
13.8%
2 364552
12.6%
3 257889
8.9%
5 239468
8.3%
0 188257
 
6.5%
6 27601
 
1.0%
9 3108
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2901496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 515267
17.8%
N 482329
16.6%
P 422084
14.5%
1 400941
13.8%
2 364552
12.6%
3 257889
8.9%
5 239468
8.3%
0 188257
 
6.5%
6 27601
 
1.0%
9 3108
 
0.1%

playingPosition
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing52010
Missing (%)4.8%
Memory size158.6 MiB
f
571512 
d
312592 
g
156556 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1040660
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowd
2nd rowd
3rd rowd
4th rowd
5th rowd

Common Values

ValueCountFrequency (%)
f 571512
52.3%
d 312592
28.6%
g 156556
 
14.3%
(Missing) 52010
 
4.8%

Length

2025-02-18T12:32:10.128888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-18T12:32:10.152399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
f 571512
54.9%
d 312592
30.0%
g 156556
 
15.0%

Most occurring characters

ValueCountFrequency (%)
f 571512
54.9%
d 312592
30.0%
g 156556
 
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1040660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 571512
54.9%
d 312592
30.0%
g 156556
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1040660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 571512
54.9%
d 312592
30.0%
g 156556
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1040660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 571512
54.9%
d 312592
30.0%
g 156556
 
15.0%

speedDown_end
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size98.9 MiB
False
1089752 
True
 
2918
ValueCountFrequency (%)
False 1089752
99.7%
True 2918
 
0.3%
2025-02-18T12:32:10.171897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

speedUp_start
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size98.9 MiB
False
1090089 
True
 
2581
ValueCountFrequency (%)
False 1090089
99.8%
True 2581
 
0.2%
2025-02-18T12:32:10.188532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

team
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size158.3 MiB
h
1092670 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1092670
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowh
2nd rowh
3rd rowh
4th rowh
5th rowh

Common Values

ValueCountFrequency (%)
h 1092670
100.0%

Length

2025-02-18T12:32:10.215024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-18T12:32:10.235066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
h 1092670
100.0%

Most occurring characters

ValueCountFrequency (%)
h 1092670
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1092670
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 1092670
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1092670
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 1092670
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1092670
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 1092670
100.0%

gap
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size98.9 MiB
False
1092649 
True
 
21
ValueCountFrequency (%)
False 1092649
> 99.9%
True 21
 
< 0.1%
2025-02-18T12:32:10.248931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

curvature
Real number (ℝ)

High correlation  Skewed 

Distinct1092634
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.733934
Minimum3.919 × 10-7
Maximum9199602.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:10.285368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.919 × 10-7
5-th percentile0.01842476
Q10.16288097
median0.89955213
Q35.8189994
95-th percentile81.561774
Maximum9199602.7
Range9199602.7
Interquartile range (IQR)5.6561184

Descriptive statistics

Standard deviation13156.851
Coefficient of variation (CV)157.12687
Kurtosis350025.34
Mean83.733934
Median Absolute Deviation (MAD)0.85890945
Skewness562.30315
Sum91493557
Variance1.7310272 × 108
MonotonicityNot monotonic
2025-02-18T12:32:10.332923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0075024593 2
 
< 0.1%
0.049536093 2
 
< 0.1%
0.0079261455 2
 
< 0.1%
0.1356174439 2
 
< 0.1%
0.0124226722 2
 
< 0.1%
0.0601517462 2
 
< 0.1%
0.2571667203 2
 
< 0.1%
0.0021790241 2
 
< 0.1%
0.3680241252 2
 
< 0.1%
0.0087637069 2
 
< 0.1%
Other values (1092624) 1092650
> 99.9%
ValueCountFrequency (%)
3.919 × 10-71
< 0.1%
3.974 × 10-71
< 0.1%
6.744 × 10-71
< 0.1%
1.033 × 10-61
< 0.1%
1.5624 × 10-61
< 0.1%
1.6564 × 10-61
< 0.1%
1.7324 × 10-61
< 0.1%
1.8138 × 10-61
< 0.1%
2.0989 × 10-61
< 0.1%
2.2575 × 10-61
< 0.1%
ValueCountFrequency (%)
9199602.733 1
< 0.1%
7735896.116 1
< 0.1%
5072934.351 1
< 0.1%
2590313.47 1
< 0.1%
1172319.686 1
< 0.1%
1099709.324 1
< 0.1%
1028621.711 1
< 0.1%
1018936.4 1
< 0.1%
1015653.568 1
< 0.1%
980510.6356 1
< 0.1%

radius_curvature
Real number (ℝ)

High correlation  Skewed 

Distinct1092642
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.847169
Minimum1.087 × 10-7
Maximum2551472.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:10.381873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.087 × 10-7
5-th percentile0.012260645
Q10.17185085
median1.1116643
Q36.1394528
95-th percentile54.27479
Maximum2551472.6
Range2551472.6
Interquartile range (IQR)5.9676019

Descriptive statistics

Standard deviation4247.5106
Coefficient of variation (CV)99.13165
Kurtosis250012.85
Mean42.847169
Median Absolute Deviation (MAD)1.0784669
Skewness456.56404
Sum46817817
Variance18041346
MonotonicityNot monotonic
2025-02-18T12:32:10.430278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1116872411 2
 
< 0.1%
0.010327509 2
 
< 0.1%
0.1526436115 2
 
< 0.1%
0.488939947 2
 
< 0.1%
0.0022343889 2
 
< 0.1%
3.888528029 2
 
< 0.1%
0.0011950606 2
 
< 0.1%
0.1655383563 2
 
< 0.1%
0.0037412785 2
 
< 0.1%
0.0759868426 2
 
< 0.1%
Other values (1092632) 1092650
> 99.9%
ValueCountFrequency (%)
1.087 × 10-71
< 0.1%
1.293 × 10-71
< 0.1%
1.971 × 10-71
< 0.1%
3.861 × 10-71
< 0.1%
8.53 × 10-71
< 0.1%
9.093 × 10-71
< 0.1%
9.722 × 10-71
< 0.1%
9.814 × 10-71
< 0.1%
9.846 × 10-71
< 0.1%
1.0199 × 10-61
< 0.1%
ValueCountFrequency (%)
2551472.587 1
< 0.1%
2516051.792 1
< 0.1%
1482866.474 1
< 0.1%
968044.2632 1
< 0.1%
640029.4874 1
< 0.1%
603722.8256 1
< 0.1%
577239.5292 1
< 0.1%
551317.6716 1
< 0.1%
476442.4704 1
< 0.1%
442975.4984 1
< 0.1%

a_tot
Real number (ℝ)

High correlation 

Distinct1092460
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30272877
Minimum4.2579 × 10-6
Maximum5.4586228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:10.478130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.2579 × 10-6
5-th percentile0.007292521
Q10.033808733
median0.10329289
Q30.35448873
95-th percentile1.3128956
Maximum5.4586228
Range5.4586185
Interquartile range (IQR)0.32068

Descriptive statistics

Standard deviation0.46478075
Coefficient of variation (CV)1.5353042
Kurtosis9.3314211
Mean0.30272877
Median Absolute Deviation (MAD)0.086677139
Skewness2.7050949
Sum330782.65
Variance0.21602115
MonotonicityNot monotonic
2025-02-18T12:32:10.589673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0354509241 2
 
< 0.1%
0.0311417935 2
 
< 0.1%
0.0716450757 2
 
< 0.1%
0.1262207148 2
 
< 0.1%
0.0113644351 2
 
< 0.1%
0.0077354281 2
 
< 0.1%
0.0678472453 2
 
< 0.1%
0.0486044685 2
 
< 0.1%
0.0043005243 2
 
< 0.1%
0.0344395255 2
 
< 0.1%
Other values (1092450) 1092650
> 99.9%
ValueCountFrequency (%)
4.2579 × 10-61
< 0.1%
4.3811 × 10-61
< 0.1%
7.4536 × 10-61
< 0.1%
1.07049 × 10-51
< 0.1%
1.19502 × 10-51
< 0.1%
1.52019 × 10-51
< 0.1%
1.68111 × 10-51
< 0.1%
1.764 × 10-51
< 0.1%
1.82054 × 10-51
< 0.1%
2.17662 × 10-51
< 0.1%
ValueCountFrequency (%)
5.458622759 1
< 0.1%
5.417963641 1
< 0.1%
5.417533069 1
< 0.1%
5.396285835 1
< 0.1%
5.393670375 1
< 0.1%
5.351071084 1
< 0.1%
5.350338456 1
< 0.1%
5.253977765 1
< 0.1%
5.24418743 1
< 0.1%
5.244105294 1
< 0.1%

a_centripetal
Real number (ℝ)

High correlation 

Distinct1092039
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15506172
Minimum3 × 10-9
Maximum3.8521343
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:10.637413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3 × 10-9
5-th percentile0.0013722975
Q10.009337079
median0.036098477
Q30.14313854
95-th percentile0.78923366
Maximum3.8521343
Range3.8521343
Interquartile range (IQR)0.13380146

Descriptive statistics

Standard deviation0.29206622
Coefficient of variation (CV)1.8835482
Kurtosis14.059531
Mean0.15506172
Median Absolute Deviation (MAD)0.032333392
Skewness3.3126872
Sum169431.29
Variance0.085302675
MonotonicityNot monotonic
2025-02-18T12:32:10.689595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0031242231 3
 
< 0.1%
0.0063637644 3
 
< 0.1%
0.0005743688 2
 
< 0.1%
0.0090283056 2
 
< 0.1%
0.0002849394 2
 
< 0.1%
0.0027295793 2
 
< 0.1%
5.37534 × 10-52
 
< 0.1%
0.0041783333 2
 
< 0.1%
0.0723216991 2
 
< 0.1%
0.0011617057 2
 
< 0.1%
Other values (1092029) 1092648
> 99.9%
ValueCountFrequency (%)
3 × 10-91
< 0.1%
1.14 × 10-81
< 0.1%
1.23 × 10-81
< 0.1%
2.84 × 10-81
< 0.1%
7.47 × 10-81
< 0.1%
7.99 × 10-81
< 0.1%
9.88 × 10-81
< 0.1%
1.583 × 10-71
< 0.1%
2.101 × 10-71
< 0.1%
2.168 × 10-71
< 0.1%
ValueCountFrequency (%)
3.852134349 1
< 0.1%
3.83011068 1
< 0.1%
3.829049377 1
< 0.1%
3.801365795 1
< 0.1%
3.790117444 1
< 0.1%
3.751364191 1
< 0.1%
3.737777324 1
< 0.1%
3.700659808 1
< 0.1%
3.699197537 1
< 0.1%
3.686770214 1
< 0.1%

g_force
Real number (ℝ)

High correlation 

Distinct1090659
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0308592
Minimum1.0000004
Maximum1.5564345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:10.739319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.0000004
5-th percentile1.0007434
Q11.0034464
median1.0105293
Q31.0361354
95-th percentile1.1338324
Maximum1.5564345
Range0.5564341
Interquartile range (IQR)0.032689092

Descriptive statistics

Standard deviation0.047378262
Coefficient of variation (CV)0.045959974
Kurtosis9.3314211
Mean1.0308592
Median Absolute Deviation (MAD)0.0088355902
Skewness2.7050949
Sum1126388.9
Variance0.0022446997
MonotonicityNot monotonic
2025-02-18T12:32:10.784706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.00218179 3
 
< 0.1%
1.00068181 3
 
< 0.1%
1.001922077 3
 
< 0.1%
1.001454141 3
 
< 0.1%
1.000891265 3
 
< 0.1%
1.009043227 2
 
< 0.1%
1.0002894 2
 
< 0.1%
1.002290294 2
 
< 0.1%
1.002680414 2
 
< 0.1%
1.002128613 2
 
< 0.1%
Other values (1090649) 1092645
> 99.9%
ValueCountFrequency (%)
1.000000434 1
< 0.1%
1.000000447 1
< 0.1%
1.00000076 1
< 0.1%
1.000001091 1
< 0.1%
1.000001218 1
< 0.1%
1.00000155 1
< 0.1%
1.000001714 1
< 0.1%
1.000001798 1
< 0.1%
1.000001856 1
< 0.1%
1.000002219 1
< 0.1%
ValueCountFrequency (%)
1.556434532 1
< 0.1%
1.552289872 1
< 0.1%
1.55224598 1
< 0.1%
1.550080106 1
< 0.1%
1.549813494 1
< 0.1%
1.545471058 1
< 0.1%
1.545396377 1
< 0.1%
1.535573676 1
< 0.1%
1.534575681 1
< 0.1%
1.534567308 1
< 0.1%

lean
Real number (ℝ)

High correlation 

Distinct1086700
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5550336
Minimum1.1966212
Maximum1.5707963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:10.830804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1966212
5-th percentile1.4905173
Q11.5562063
median1.5671166
Q31.5698445
95-th percentile1.5706564
Maximum1.5707963
Range0.37417516
Interquartile range (IQR)0.013638258

Descriptive statistics

Standard deviation0.02957383
Coefficient of variation (CV)0.01901813
Kurtosis13.507879
Mean1.5550336
Median Absolute Deviation (MAD)0.0032959389
Skewness-3.2690362
Sum1699138.5
Variance0.00087461144
MonotonicityNot monotonic
2025-02-18T12:32:10.880492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.569070655 4
 
< 0.1%
1.570717341 4
 
< 0.1%
1.570136209 3
 
< 0.1%
1.570455536 3
 
< 0.1%
1.57042199 3
 
< 0.1%
1.570246226 3
 
< 0.1%
1.569909532 3
 
< 0.1%
1.570764221 3
 
< 0.1%
1.57042774 3
 
< 0.1%
1.570787069 3
 
< 0.1%
Other values (1086690) 1092638
> 99.9%
ValueCountFrequency (%)
1.196621164 1
< 0.1%
1.19856775 1
< 0.1%
1.198661629 1
< 0.1%
1.201112854 1
< 0.1%
1.202110166 1
< 0.1%
1.205552025 1
< 0.1%
1.206760892 1
< 0.1%
1.210069033 1
< 0.1%
1.210199529 1
< 0.1%
1.211309089 1
< 0.1%
ValueCountFrequency (%)
1.570796327 1
< 0.1%
1.570796326 1
< 0.1%
1.570796325 1
< 0.1%
1.570796324 1
< 0.1%
1.570796319 1
< 0.1%
1.570796319 1
< 0.1%
1.570796317 1
< 0.1%
1.570796311 1
< 0.1%
1.570796305 1
< 0.1%
1.570796305 1
< 0.1%

skatingEdge
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size163.3 MiB
linear
957236 
right
 
69750
left
 
65684

Length

Max length6
Median length6
Mean length5.8159389
Min length4

Characters and Unicode

Total characters6354902
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlinear
2nd rowlinear
3rd rowlinear
4th rowlinear
5th rowlinear

Common Values

ValueCountFrequency (%)
linear 957236
87.6%
right 69750
 
6.4%
left 65684
 
6.0%

Length

2025-02-18T12:32:10.927934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-18T12:32:10.953857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
linear 957236
87.6%
right 69750
 
6.4%
left 65684
 
6.0%

Most occurring characters

ValueCountFrequency (%)
i 1026986
16.2%
r 1026986
16.2%
l 1022920
16.1%
e 1022920
16.1%
n 957236
15.1%
a 957236
15.1%
t 135434
 
2.1%
g 69750
 
1.1%
h 69750
 
1.1%
f 65684
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6354902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1026986
16.2%
r 1026986
16.2%
l 1022920
16.1%
e 1022920
16.1%
n 957236
15.1%
a 957236
15.1%
t 135434
 
2.1%
g 69750
 
1.1%
h 69750
 
1.1%
f 65684
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6354902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1026986
16.2%
r 1026986
16.2%
l 1022920
16.1%
e 1022920
16.1%
n 957236
15.1%
a 957236
15.1%
t 135434
 
2.1%
g 69750
 
1.1%
h 69750
 
1.1%
f 65684
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6354902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1026986
16.2%
r 1026986
16.2%
l 1022920
16.1%
e 1022920
16.1%
n 957236
15.1%
a 957236
15.1%
t 135434
 
2.1%
g 69750
 
1.1%
h 69750
 
1.1%
f 65684
 
1.0%

g_force_avg
Real number (ℝ)

High correlation 

Distinct532
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0329772
Minimum1
Maximum1.581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:10.988212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.001
Q11.004
median1.011
Q31.039
95-th percentile1.142
Maximum1.581
Range0.581
Interquartile range (IQR)0.035

Descriptive statistics

Standard deviation0.05013481
Coefficient of variation (CV)0.048534286
Kurtosis9.0582955
Mean1.0329772
Median Absolute Deviation (MAD)0.009
Skewness2.6737134
Sum1128703.2
Variance0.0025134992
MonotonicityNot monotonic
2025-02-18T12:32:11.034371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.001 88874
 
8.1%
1.002 78490
 
7.2%
1.003 66006
 
6.0%
1.004 56495
 
5.2%
1.005 48608
 
4.4%
1.006 42548
 
3.9%
1.007 37151
 
3.4%
1.008 31994
 
2.9%
1.009 28678
 
2.6%
1.01 26132
 
2.4%
Other values (522) 587694
53.8%
ValueCountFrequency (%)
1 20089
 
1.8%
1.001 88874
8.1%
1.002 78490
7.2%
1.003 66006
6.0%
1.004 56495
5.2%
1.005 48608
4.4%
1.006 42548
3.9%
1.007 37151
3.4%
1.008 31994
 
2.9%
1.009 28678
 
2.6%
ValueCountFrequency (%)
1.581 2
< 0.1%
1.578 1
< 0.1%
1.577 1
< 0.1%
1.572 2
< 0.1%
1.563 2
< 0.1%
1.561 1
< 0.1%
1.56 1
< 0.1%
1.558 2
< 0.1%
1.554 1
< 0.1%
1.553 2
< 0.1%

g_force_peak
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size98.9 MiB
False
1013781 
True
 
78889
ValueCountFrequency (%)
False 1013781
92.8%
True 78889
 
7.2%
2025-02-18T12:32:11.064809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

deaccel
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size98.9 MiB
True
549004 
False
543666 
ValueCountFrequency (%)
True 549004
50.2%
False 543666
49.8%
2025-02-18T12:32:11.080761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

sustained_speed
Real number (ℝ)

High correlation 

Distinct1018508
Distinct (%)93.2%
Missing30
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.61026501
Minimum2.93626 × 10-5
Maximum7.762406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:11.116088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.93626 × 10-5
5-th percentile0.0079825109
Q10.043697464
median0.15646025
Q30.78222875
95-th percentile2.6556325
Maximum7.762406
Range7.7623766
Interquartile range (IQR)0.73853128

Descriptive statistics

Standard deviation0.93803254
Coefficient of variation (CV)1.5370905
Kurtosis5.699355
Mean0.61026501
Median Absolute Deviation (MAD)0.14011978
Skewness2.2542236
Sum666799.96
Variance0.87990504
MonotonicityNot monotonic
2025-02-18T12:32:11.163514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.004114103 10
 
< 0.1%
0.0042605341 6
 
< 0.1%
0.0812752075 6
 
< 0.1%
0.2180283631 6
 
< 0.1%
0.0347769165 6
 
< 0.1%
0.0065651896 6
 
< 0.1%
0.0125166657 6
 
< 0.1%
0.004379134 6
 
< 0.1%
0.0421515427 6
 
< 0.1%
0.0105454596 6
 
< 0.1%
Other values (1018498) 1092576
> 99.9%
(Missing) 30
 
< 0.1%
ValueCountFrequency (%)
2.93626 × 10-55
< 0.1%
3.51253 × 10-55
< 0.1%
3.60098 × 10-55
< 0.1%
5.86068 × 10-55
< 0.1%
6.44784 × 10-55
< 0.1%
6.56055 × 10-55
< 0.1%
7.67525 × 10-55
< 0.1%
7.72989 × 10-55
< 0.1%
8.0837 × 10-55
< 0.1%
9.13078 × 10-55
< 0.1%
ValueCountFrequency (%)
7.762406 1
< 0.1%
7.749944724 1
< 0.1%
7.747832728 1
< 0.1%
7.729144553 1
< 0.1%
7.728324315 1
< 0.1%
7.703495124 1
< 0.1%
7.703287693 1
< 0.1%
7.674338774 1
< 0.1%
7.670600355 1
< 0.1%
7.639983153 1
< 0.1%

anomaly
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size158.3 MiB
1
1092595 
-1
 
75

Length

Max length2
Median length1
Mean length1.0000686
Min length1

Characters and Unicode

Total characters1092745
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1092595
> 99.9%
-1 75
 
< 0.1%

Length

2025-02-18T12:32:11.207317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-18T12:32:11.229246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1092670
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1092670
> 99.9%
- 75
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1092745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1092670
> 99.9%
- 75
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1092745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1092670
> 99.9%
- 75
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1092745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1092670
> 99.9%
- 75
 
< 0.1%

playerShift
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.6 MiB
on
893363 
off
188257 
entered
 
5715
changing
 
5335

Length

Max length8
Median length2
Mean length2.2277376
Min length2

Characters and Unicode

Total characters2434182
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowentered
2nd rowentered
3rd rowentered
4th rowentered
5th rowentered

Common Values

ValueCountFrequency (%)
on 893363
81.8%
off 188257
 
17.2%
entered 5715
 
0.5%
changing 5335
 
0.5%

Length

2025-02-18T12:32:11.258605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-18T12:32:11.284480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
on 893363
81.8%
off 188257
 
17.2%
entered 5715
 
0.5%
changing 5335
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 1081620
44.4%
n 909748
37.4%
f 376514
 
15.5%
e 17145
 
0.7%
g 10670
 
0.4%
t 5715
 
0.2%
r 5715
 
0.2%
d 5715
 
0.2%
c 5335
 
0.2%
h 5335
 
0.2%
Other values (2) 10670
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2434182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1081620
44.4%
n 909748
37.4%
f 376514
 
15.5%
e 17145
 
0.7%
g 10670
 
0.4%
t 5715
 
0.2%
r 5715
 
0.2%
d 5715
 
0.2%
c 5335
 
0.2%
h 5335
 
0.2%
Other values (2) 10670
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2434182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1081620
44.4%
n 909748
37.4%
f 376514
 
15.5%
e 17145
 
0.7%
g 10670
 
0.4%
t 5715
 
0.2%
r 5715
 
0.2%
d 5715
 
0.2%
c 5335
 
0.2%
h 5335
 
0.2%
Other values (2) 10670
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2434182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1081620
44.4%
n 909748
37.4%
f 376514
 
15.5%
e 17145
 
0.7%
g 10670
 
0.4%
t 5715
 
0.2%
r 5715
 
0.2%
d 5715
 
0.2%
c 5335
 
0.2%
h 5335
 
0.2%
Other values (2) 10670
 
0.4%

playerShiftNum
Real number (ℝ)

High correlation  Zeros 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3351478
Minimum0
Maximum31
Zeros303635
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:11.316459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q313
95-th percentile24
Maximum31
Range31
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.8512957
Coefficient of variation (CV)1.0703664
Kurtosis-0.028643636
Mean7.3351478
Median Absolute Deviation (MAD)4
Skewness0.98095815
Sum8014896
Variance61.642844
MonotonicityNot monotonic
2025-02-18T12:32:11.357031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 303635
27.8%
4 76357
 
7.0%
1 64602
 
5.9%
2 64005
 
5.9%
7 63941
 
5.9%
3 46376
 
4.2%
5 42908
 
3.9%
13 37167
 
3.4%
14 36098
 
3.3%
12 33328
 
3.1%
Other values (22) 324253
29.7%
ValueCountFrequency (%)
0 303635
27.8%
1 64602
 
5.9%
2 64005
 
5.9%
3 46376
 
4.2%
4 76357
 
7.0%
5 42908
 
3.9%
6 18549
 
1.7%
7 63941
 
5.9%
8 17878
 
1.6%
9 24489
 
2.2%
ValueCountFrequency (%)
31 3974
 
0.4%
30 2422
 
0.2%
29 2743
 
0.3%
28 6251
 
0.6%
27 8139
 
0.7%
26 4853
 
0.4%
25 10202
0.9%
24 20423
1.9%
23 12734
1.2%
22 17999
1.6%

toi
Real number (ℝ)

High correlation 

Distinct48803
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2339.6261
Minimum0
Maximum4880.2
Zeros70
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size106.2 MiB
2025-02-18T12:32:11.401646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile255.5
Q11221
median2440.2
Q33490.1
95-th percentile4119
Maximum4880.2
Range4880.2
Interquartile range (IQR)2269.1

Descriptive statistics

Standard deviation1275.5572
Coefficient of variation (CV)0.54519702
Kurtosis-1.2216543
Mean2339.6261
Median Absolute Deviation (MAD)1116.2
Skewness-0.1615516
Sum2.5564393 × 109
Variance1627046.1
MonotonicityNot monotonic
2025-02-18T12:32:11.448375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3558.5 9069
 
0.8%
3836.9 5026
 
0.5%
3931.2 4965
 
0.5%
4055.9 4874
 
0.4%
4095.3 4839
 
0.4%
4484.5 3528
 
0.3%
4294.3 3388
 
0.3%
3326.8 2144
 
0.2%
3577.7 2069
 
0.2%
3929.8 2069
 
0.2%
Other values (48793) 1050699
96.2%
ValueCountFrequency (%)
0 70
< 0.1%
0.1 21
 
< 0.1%
0.2 21
 
< 0.1%
0.3 21
 
< 0.1%
0.4 21
 
< 0.1%
0.5 21
 
< 0.1%
0.6 21
 
< 0.1%
0.7 21
 
< 0.1%
0.8 21
 
< 0.1%
0.9 21
 
< 0.1%
ValueCountFrequency (%)
4880.2 1
< 0.1%
4880.1 1
< 0.1%
4880 1
< 0.1%
4879.9 1
< 0.1%
4879.8 1
< 0.1%
4879.7 1
< 0.1%
4879.6 1
< 0.1%
4879.5 1
< 0.1%
4879.4 1
< 0.1%
4879.3 1
< 0.1%

Interactions

2025-02-18T12:31:55.426158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:38.154568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:44.627411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:50.908297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:57.272217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:03.680891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:09.906681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:16.204560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:22.574400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:28.796658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:35.250989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:41.596066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:47.875614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:54.192461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:00.506450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:53.395144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:59.632592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:05.992459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:12.276061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:18.733059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:25.031544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:31.465652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-18T12:30:51.510189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:57.609626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:03.959762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:10.312447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:16.787176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:23.081769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:29.483739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:35.910132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:45.932620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:53.486423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:32:00.143454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:42.848911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:49.244823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:55.573830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:01.998485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:08.226353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:14.542311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:20.849290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:27.123644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:33.545088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:39.923296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:46.179330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:52.512922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:58.896840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:51.764095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:57.863032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:04.219874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:10.572131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:17.053010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:23.332249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:29.752005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:36.166750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:46.409786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:53.739209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:32:00.403292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:43.099955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:49.494001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:55.833180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:02.256329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:08.485021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:14.792711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:21.104322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:27.376551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:33.801973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:40.174963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:46.435488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:52.768996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:59.146766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:52.009396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:58.181470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:04.480101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:10.821147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:17.309853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:23.590082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:30.003321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:36.420628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:46.897752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:53.993694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:32:00.658732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:43.424588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:49.752520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:56.092273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:02.517070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:08.742062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:15.042577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:21.359178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:27.633829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:34.060788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:40.431973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:46.689948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:53.024477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:59.399863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:52.259330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:58.467504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:04.731642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:11.073479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:17.565353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:23.845381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:30.263240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:36.677209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:47.386872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:54.250246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:32:01.025328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:43.786498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:50.103112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:56.446423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:02.870773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:09.092445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:15.398486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:21.717238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:27.986151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:34.414507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:40.788287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:47.046915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:53.379418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:59.737387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:52.611475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:58.824347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:05.087608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:11.455599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:17.917204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:24.199841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:30.634437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:37.030036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:47.873713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:54.616548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:32:01.333154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:44.101141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:50.398135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:56.753355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:03.168490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:09.393535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:15.697078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:22.040398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:28.287811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:34.727984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:41.087044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:47.344695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:53.680288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:00.017534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:52.900589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:59.116364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:05.392588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:11.757565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:18.215875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:24.505504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:30.941733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:37.325949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:48.323737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:54.914776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:32:01.604811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:44.374663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:50.653904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:28:57.018619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:03.426208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:09.652718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:15.953545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:22.316927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:28.545533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:34.998630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:41.344708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:47.604909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:29:53.944268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:00.261298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:53.147922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:30:59.378290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:05.665099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:12.019387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:18.479194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:24.771434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:31.210002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:37.588823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:48.781893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-18T12:31:55.171250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-18T12:32:11.567397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
a_centripetala_totaccelerationanomalyaxaycurvaturedeacceldisplacementg_forceg_force_avgg_force_peakgapleanplayerIdplayerShiftplayerShiftNumplayingPositionqradius_curvatureskatingAngleskatingEdgespeedspeedDown_endspeedUpspeedUp_startsuperframesustained_speedtagIdtimestamptoitotalDistancevxvyxy
a_centripetal1.0000.8860.8550.0130.005-0.004-0.2760.0090.7210.8860.8660.0500.000-1.0000.0720.099-0.0050.0850.0070.276-0.0260.0740.7210.0300.0150.0480.0070.7140.072-0.121-0.1240.0210.0170.001-0.0170.260
a_tot0.8861.0000.9970.0100.0070.004-0.5010.0180.8271.0000.9750.0580.001-0.8860.0830.119-0.0010.0980.0090.501-0.0210.0690.8280.0500.0280.0800.0090.8060.083-0.129-0.1320.0270.0220.001-0.0080.280
acceleration0.8550.9971.0000.0100.0080.005-0.5230.0220.8270.9970.9720.0610.007-0.8550.0860.1280.0010.1030.0090.523-0.0200.0620.8280.0650.0320.0850.0090.8030.086-0.127-0.1300.0270.0230.001-0.0070.277
anomaly0.0130.0100.0101.0000.0060.0140.0000.0020.0000.0100.0160.0020.0000.0130.0200.0040.0150.0040.0240.0000.0030.0030.5580.0000.0010.0000.0290.9130.0200.0130.0110.0120.4200.0100.0090.010
ax0.0050.0070.0080.0061.0000.027-0.0060.0120.0060.0070.0050.0380.005-0.0050.0650.111-0.0050.083-0.0010.006-0.0040.0410.0060.0440.0230.060-0.0010.0050.065-0.004-0.005-0.003-0.0010.028-0.116-0.011
ay-0.0040.0040.0050.0140.0271.000-0.0070.0240.0030.0040.0040.0310.0030.0040.0660.1100.0020.0760.0000.0070.0160.0420.0030.0410.0200.0620.0000.0020.0660.0030.0030.003-0.0230.000-0.002-0.189
curvature-0.276-0.501-0.5230.000-0.006-0.0071.0000.000-0.845-0.501-0.5430.0010.0000.2760.0010.000-0.0220.000-0.004-1.000-0.0040.007-0.8460.0000.0020.000-0.004-0.8440.0010.0640.069-0.0360.0080.011-0.003-0.159
deaccel0.0090.0180.0220.0020.0120.0240.0001.0000.0000.0180.0370.0300.0010.0090.0070.0300.0040.0000.0000.0020.0040.0110.0110.0340.5440.0320.0010.0240.0070.0070.0060.0080.0130.0120.0180.010
displacement0.7210.8270.8270.0000.0060.003-0.8450.0001.0000.8270.8480.0001.000-0.7210.0000.0180.0090.0000.0070.845-0.0120.0100.9990.0300.0010.0300.0070.9950.000-0.116-0.1200.0340.001-0.010-0.0050.256
g_force0.8861.0000.9970.0100.0070.004-0.5010.0180.8271.0000.9750.0580.001-0.8860.0830.119-0.0010.0980.0090.501-0.0210.0690.8280.0500.0280.0800.0090.8060.083-0.129-0.1320.0270.0220.001-0.0080.280
g_force_avg0.8660.9750.9720.0160.0050.004-0.5430.0370.8480.9751.0000.0600.000-0.8660.0830.121-0.0010.0990.0080.543-0.0210.0540.8500.0490.0640.0840.0080.8250.083-0.132-0.1350.0260.021-0.000-0.0080.285
g_force_peak0.0500.0580.0610.0020.0380.0310.0010.0300.0000.0580.0601.0000.0000.0510.0130.0260.0110.0060.0030.0000.0350.0850.0710.0050.0430.0040.0020.0730.0130.0270.0230.0140.0580.0640.0270.033
gap0.0000.0010.0070.0000.0050.0030.0000.0011.0000.0010.0000.0001.0000.0000.0000.0230.0060.0000.0000.0000.0000.0120.1050.0220.0000.0100.0000.0000.0000.0110.0060.0070.0360.0080.0080.003
lean-1.000-0.886-0.8550.013-0.0050.0040.2760.009-0.721-0.886-0.8660.0510.0001.0000.0730.1010.0050.086-0.007-0.2760.0260.075-0.7210.0300.0160.049-0.007-0.7140.0730.1210.124-0.021-0.017-0.0010.017-0.260
playerId0.0720.0830.0860.0200.0650.0660.0010.0070.0000.0830.0830.0130.0000.0731.0000.0850.3211.0000.0110.0000.0150.0370.0690.0170.0040.0180.0050.0701.0000.0070.1190.2720.0590.0650.2360.136
playerShift0.0990.1190.1280.0040.1110.1100.0000.0300.0180.1190.1210.0260.0230.1010.0851.0000.1730.0710.0080.0000.0140.0210.1460.0180.0490.0310.0070.1500.0850.2550.2290.2240.1200.1380.3110.484
playerShiftNum-0.005-0.0010.0010.015-0.0050.002-0.0220.0040.009-0.001-0.0010.0110.0060.0050.3210.1731.0000.268-0.0220.0220.0140.0190.0080.0030.0030.003-0.0220.0090.3210.7650.7100.876-0.0000.003-0.077-0.203
playingPosition0.0850.0980.1030.0040.0830.0760.0000.0000.0000.0980.0990.0060.0000.0861.0000.0710.2681.0000.0060.0000.0070.0040.0900.0110.0000.0120.0010.0931.0000.0020.0880.2740.0780.0810.1740.142
q0.0070.0090.0090.024-0.0010.000-0.0040.0000.0070.0090.0080.0030.000-0.0070.0110.008-0.0220.0061.0000.0040.0010.0030.0070.0040.0020.0010.9990.0060.011-0.035-0.035-0.0230.0030.0010.0100.012
radius_curvature0.2760.5010.5230.0000.0060.007-1.0000.0020.8450.5010.5430.0000.000-0.2760.0000.0000.0220.0000.0041.0000.0040.0000.8460.0000.0020.0000.0040.8440.000-0.064-0.0690.036-0.008-0.0110.0030.159
skatingAngle-0.026-0.021-0.0200.003-0.0040.016-0.0040.004-0.012-0.021-0.0210.0350.0000.0260.0150.0140.0140.0070.0010.0041.0000.314-0.0110.0030.0480.0030.001-0.0120.0150.0080.0060.012-0.0100.0020.001-0.016
skatingEdge0.0740.0690.0620.0030.0410.0420.0070.0110.0100.0690.0540.0850.0120.0750.0370.0210.0190.0040.0030.0000.3141.0000.1180.0040.0220.0110.0030.1190.0370.0250.0240.0250.1040.1070.0350.023
speed0.7210.8280.8280.5580.0060.003-0.8460.0110.9990.8280.8500.0710.105-0.7210.0690.1460.0080.0900.0070.846-0.0110.1181.0000.0290.0140.1330.0070.9940.069-0.117-0.1220.0320.002-0.009-0.0070.258
speedDown_end0.0300.0500.0650.0000.0440.0410.0000.0340.0300.0500.0490.0050.0220.0300.0170.0180.0030.0110.0040.0000.0030.0040.0291.0000.0500.0020.0000.1300.0170.0140.0150.0100.0430.0490.0240.021
speedUp0.0150.0280.0320.0010.0230.0200.0020.5440.0010.0280.0640.0430.0000.0160.0040.0490.0030.0000.0020.0020.0480.0220.0140.0501.0000.0500.0020.0230.0040.0090.0070.0080.0130.0170.0170.022
speedUp_start0.0480.0800.0850.0000.0600.0620.0000.0320.0300.0800.0840.0040.0100.0490.0180.0310.0030.0120.0010.0000.0030.0110.1330.0020.0501.0000.0000.1380.0180.0170.0170.0080.0500.0740.0220.028
superframe0.0070.0090.0090.029-0.0010.000-0.0040.0010.0070.0090.0080.0020.000-0.0070.0050.007-0.0220.0010.9990.0040.0010.0030.0070.0000.0020.0001.0000.0060.005-0.035-0.035-0.0230.0030.0010.0100.012
sustained_speed0.7140.8060.8030.9130.0050.002-0.8440.0240.9950.8060.8250.0730.000-0.7140.0700.1500.0090.0930.0060.844-0.0120.1190.9940.1300.0230.1380.0061.0000.070-0.114-0.1190.0330.000-0.011-0.0080.253
tagId0.0720.0830.0860.0200.0650.0660.0010.0070.0000.0830.0830.0130.0000.0731.0000.0850.3211.0000.0110.0000.0150.0370.0690.0170.0040.0180.0050.0701.0000.0070.1190.2720.0590.0650.2360.136
timestamp-0.121-0.129-0.1270.013-0.0040.0030.0640.007-0.116-0.129-0.1320.0270.0110.1210.0070.2550.7650.002-0.035-0.0640.0080.025-0.1170.0140.0090.017-0.035-0.1140.0071.0000.9900.820-0.006-0.0060.071-0.241
toi-0.124-0.132-0.1300.011-0.0050.0030.0690.006-0.120-0.132-0.1350.0230.0060.1240.1190.2290.7100.088-0.035-0.0690.0060.024-0.1220.0150.0070.017-0.035-0.1190.1190.9901.0000.790-0.006-0.0090.089-0.235
totalDistance0.0210.0270.0270.012-0.0030.003-0.0360.0080.0340.0270.0260.0140.007-0.0210.2720.2240.8760.274-0.0230.0360.0120.0250.0320.0100.0080.008-0.0230.0330.2720.8200.7901.000-0.005-0.0070.012-0.218
vx0.0170.0220.0230.420-0.001-0.0230.0080.0130.0010.0220.0210.0580.036-0.0170.0590.120-0.0000.0780.003-0.008-0.0100.1040.0020.0430.0130.0500.0030.0000.059-0.006-0.006-0.0051.0000.026-0.0020.027
vy0.0010.0010.0010.0100.0280.0000.0110.012-0.0100.001-0.0000.0640.008-0.0010.0650.1380.0030.0810.001-0.0110.0020.107-0.0090.0490.0170.0740.001-0.0110.065-0.006-0.009-0.0070.0261.000-0.0250.028
x-0.017-0.008-0.0070.009-0.116-0.002-0.0030.018-0.005-0.008-0.0080.0270.0080.0170.2360.311-0.0770.1740.0100.0030.0010.035-0.0070.0240.0170.0220.010-0.0080.2360.0710.0890.012-0.002-0.0251.000-0.093
y0.2600.2800.2770.010-0.011-0.189-0.1590.0100.2560.2800.2850.0330.003-0.2600.1360.484-0.2030.1420.0120.159-0.0160.0230.2580.0210.0220.0280.0120.2530.136-0.241-0.235-0.2180.0270.028-0.0931.000

Missing values

2025-02-18T12:32:01.915332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-18T12:32:04.341441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-18T12:32:06.785781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

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00012173331752580019.658475-0.4470940.1347370.747618000.7596620.0854580.0734740.0436420.0000000.000000h26on0.000000FalseP33dFalseFalsehFalse0.1118878.9375930.1071080.0645681.0109181.564215linear1.011TrueFalse0.7596621entered00.1
10012173331752590019.672316-0.3721150.1420860.751957000.7652630.0853410.0734890.0433870.0762460.076246h26on-0.480524TrueP33dFalseFalsehFalse0.1095509.1282500.1067660.0641551.0108831.564257linear1.011FalseFalse0.7596621entered00.2
20012173331752600019.686892-0.2967050.1494300.756244000.7708660.0850420.0734410.0428790.1530510.076806h26on-0.473842TrueP33dFalseFalsehFalse0.1072579.3234230.1062750.0637361.0108331.564299linear1.011FalseFalse0.7596621entered00.3
30012173331752610019.702201-0.2208700.1567610.760450000.7764400.0845140.0733060.0420560.2304160.077365h26on-0.467182TrueP33dFalseFalsehFalse0.1050099.5230080.1055940.0633051.0107641.564343linear1.011FalseFalse0.7596621entered00.4
40012173331752620019.718243-0.1446210.1640670.764536000.7819420.0837090.0730620.0408560.3083350.077919h26on-0.460752TrueP33dFalseFalsehFalse0.1028129.7264600.1046850.0628631.0106711.564388linear1.011FalseFalse0.7596621entered00.5
50012173331752630019.735011-0.0679830.1713130.768338000.7872040.0822130.0725760.0386230.3867860.078451h26on-0.454184TrueP33dFalseFalsehFalse0.1007459.9260290.1032310.0624311.0105231.564432linear1.011FalseFalse0.7652631entered00.6
60012173331752640019.7525020.0090340.1785170.771987000.7923590.0807570.0720400.0364950.4657640.078978h26on-0.447752TrueP33dFalseFalsehFalse0.09869810.1318960.1017910.0619661.0103761.564480linear1.011FalseFalse0.7708661entered00.7
70012173331752650019.7707110.0864020.1856490.775372000.7972870.0789440.0713220.0338430.5452450.079482h26on-0.441328TrueP33dFalseFalsehFalse0.09672010.3391410.1000610.0614821.0102001.564529linear1.011FalseFalse0.7764401entered00.8
80012173331752660019.7896270.1640920.1926890.778434000.8019280.0767710.0703960.0306290.6252050.079960h26on-0.434947TrueP33dFalseFalsehFalse0.09481410.5469700.0980380.0609741.0099941.564581linear1.011FalseFalse0.7819421entered00.9
90012173331752670019.8092420.2420700.1996120.781116000.8062180.0742480.0692360.0268190.7056120.080407h26on-0.426189TrueP33dFalseFalsehFalse0.09298610.7543190.0957380.0604401.0097591.564635linear1.011FalseFalse0.7872041entered01.0
tagIdtimestampxyvxvyqsuperframespeedaccelerationaxaytotalDistancedisplacementplayerIdgameStatusskatingAnglespeedUpzoneplayingPositionspeedDown_endspeedUp_startteamgapcurvatureradius_curvaturea_tota_centripetalg_forceleanskatingEdgeg_force_avgg_force_peakdeaccelsustained_speedanomalyplayerShiftplayerShiftNumtoi
109266035671733322756400-7.25782615.6789270.5736130.195956000.6061600.2647220.259715-0.051242859.9680590.059467h9on-1.260650True0gFalseFalsehFalse0.3604772.7741040.2960080.1324501.0301741.557296linear1.036FalseTrue0.5161121off34309.7
109266135671733322756500-7.19917415.6982710.5994290.190921000.6290990.2630240.258160-0.050348860.0298190.061760h9on-1.146267True0gFalseFalsehFalse0.3191813.1330150.2917850.1263211.0297441.557920linear1.035FalseTrue0.5383041off34309.7
109266235671733322756600-7.13794615.7171150.6251200.185955000.6521920.2616680.256911-0.049666860.0938800.064062h9on-1.058815True0gFalseFalsehFalse0.2841293.5195340.2882290.1208551.0293811.558477linear1.034FalseTrue0.5607981off34309.7
109266335671733322756700-7.07415515.7354640.6507130.181038000.6754270.2606120.255931-0.049172860.1602590.066378h9on-0.981904True0gFalseFalsehFalse0.2542093.9337750.2852500.1159711.0290771.558975linear1.034FalseTrue0.5835361off34309.7
109266435671733322756800-7.00780715.7533240.6762310.176153000.6987980.2598150.255183-0.048844860.2289680.068709h9on-0.913942True0gFalseFalsehFalse0.2285244.3759000.2827660.1115931.0288241.559421linear1.033FalseTrue0.6061601off34309.7
109266535671733322756900-6.93891115.7706960.7016940.171287000.7222980.2592370.254630-0.048659860.3000200.071053h9on-0.853574True0gFalseFalsehFalse0.2063474.8462000.2807020.1076541.0286141.559823linear1.033FalseTrue0.6290991off34309.7
109266635671733322757000-6.86747015.7875820.7271180.166428000.7459210.2588380.254235-0.048595860.3734290.073409h9on-0.799656True0gFalseFalsehFalse0.1870855.3451590.2789850.1040941.0284391.560186linear1.032FalseTrue0.6521921off34309.7
109266735671733322757100-6.79348915.8039810.7525140.161565000.7696630.2585760.253962-0.048628860.4492070.075777h9on-0.751217True0gFalseFalsehFalse0.1702555.8735410.2775490.1008561.0282921.560516linear1.032FalseTrue0.6754271off34309.7
109266835671733322757200-6.71696915.8198940.7778910.156691000.7935160.2584120.253774-0.048737860.5273640.078157h9on-0.707426True0gFalseFalsehFalse0.1554616.4324670.2763310.0978891.0281681.560818linear1.031FalseTrue0.6987981off34309.7
109266935671733322757300-6.63791115.8353190.8032550.151802000.8174730.2583050.253634-0.048898860.6079120.080548h9on0.000000True0gFalseFalsehFalse0.1423797.0234920.2752710.0951471.0280601.561098linear1.031FalseTrue0.7222981off34309.7